Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations

Birhanu Mengistu Aboye; Tilahun Mola Tesema

doi:doi:10.11648/j.ajbes.20241003.16

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Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations

Birhanu Mengistu Aboye^*

, Tilahun Mola Tesema

Published in American Journal of Biological and Environmental Statistics (Volume 10, Issue 3)

Received: 30 August 2024 Accepted: 18 September 2024 Published: 29 September 2024

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Abstract

Sunflower is an important agricultural crop valued for its high oil yield, versatility in culinary and industrial applications and adaptability to diverse environments. Eight advanced sunflower genotypes were tested in a randomized complete block design (RCBD) with three replications at six locations over the 2018 and 2019 seasons. The study aimed to evaluate the effects of environmental and genotypic variations using MANOVA, PCA, and correlation analysis to discover trait patterns and relationships. The MANOVA results revealed highly significant effects of genotype, environment, and their interaction on the 11 dependent variables (p < 0.001). The four principal components account for 74.23% of the total variation, with key traits such as seed yield per hectare, oil yield per hectare, days to maturity, plant height, and grain filling period significantly contributing to the overall variability. Oil yield per hectare and seed yield per hectare exhibited a very strong association (0.974). Days to maturity (DM) and grain filling period (GFP) showed a strong correlation (0.666), suggesting that longer grain filling periods may enhance both maturity and yield. Additionally, plant height (PH) and seed yield per hectare (YELDK) had a moderate correlation (0.491). Breeding programs should target traits with strong correlations to boost sunflower productivity and adaptability. Future research should prioritize selecting genotypes that perform well across diverse environments, focusing on seed yield, oil yield, and traits such as maturity and grain filling period. Additionally, breeding should incorporate disease resistance and optimize days to flowering to develop more robust and productive sunflower varieties.

Published in	American Journal of Biological and Environmental Statistics (Volume 10, Issue 3)
DOI	10.11648/j.ajbes.20241003.16
Page(s)	87-95
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Multivariate Analysis, Oil Yield, Phenotypic Variation, Seed Yield and Trait Associations

References

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APA Style

Aboye, B. M., Tesema, T. M. (2024). Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations. American Journal of Biological and Environmental Statistics, 10(3), 87-95. https://doi.org/10.11648/j.ajbes.20241003.16

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ACS Style

Aboye, B. M.; Tesema, T. M. Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations. Am. J. Biol. Environ. Stat. 2024, 10(3), 87-95. doi: 10.11648/j.ajbes.20241003.16

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AMA Style

Aboye BM, Tesema TM. Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations. Am J Biol Environ Stat. 2024;10(3):87-95. doi: 10.11648/j.ajbes.20241003.16

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@article{10.11648/j.ajbes.20241003.16,
  author = {Birhanu Mengistu Aboye and Tilahun Mola Tesema},
  title = {Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations
},
  journal = {American Journal of Biological and Environmental Statistics},
  volume = {10},
  number = {3},
  pages = {87-95},
  doi = {10.11648/j.ajbes.20241003.16},
  url = {https://doi.org/10.11648/j.ajbes.20241003.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbes.20241003.16},
  abstract = {Sunflower is an important agricultural crop valued for its high oil yield, versatility in culinary and industrial applications and adaptability to diverse environments. Eight advanced sunflower genotypes were tested in a randomized complete block design (RCBD) with three replications at six locations over the 2018 and 2019 seasons. The study aimed to evaluate the effects of environmental and genotypic variations using MANOVA, PCA, and correlation analysis to discover trait patterns and relationships. The MANOVA results revealed highly significant effects of genotype, environment, and their interaction on the 11 dependent variables (p < 0.001). The four principal components account for 74.23% of the total variation, with key traits such as seed yield per hectare, oil yield per hectare, days to maturity, plant height, and grain filling period significantly contributing to the overall variability. Oil yield per hectare and seed yield per hectare exhibited a very strong association (0.974). Days to maturity (DM) and grain filling period (GFP) showed a strong correlation (0.666), suggesting that longer grain filling periods may enhance both maturity and yield. Additionally, plant height (PH) and seed yield per hectare (YELDK) had a moderate correlation (0.491). Breeding programs should target traits with strong correlations to boost sunflower productivity and adaptability. Future research should prioritize selecting genotypes that perform well across diverse environments, focusing on seed yield, oil yield, and traits such as maturity and grain filling period. Additionally, breeding should incorporate disease resistance and optimize days to flowering to develop more robust and productive sunflower varieties.
},
 year = {2024}
}

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TY  - JOUR
T1  - Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations

AU  - Birhanu Mengistu Aboye
AU  - Tilahun Mola Tesema
Y1  - 2024/09/29
PY  - 2024
N1  - https://doi.org/10.11648/j.ajbes.20241003.16
DO  - 10.11648/j.ajbes.20241003.16
T2  - American Journal of Biological and Environmental Statistics
JF  - American Journal of Biological and Environmental Statistics
JO  - American Journal of Biological and Environmental Statistics
SP  - 87
EP  - 95
PB  - Science Publishing Group
SN  - 2471-979X
UR  - https://doi.org/10.11648/j.ajbes.20241003.16
AB  - Sunflower is an important agricultural crop valued for its high oil yield, versatility in culinary and industrial applications and adaptability to diverse environments. Eight advanced sunflower genotypes were tested in a randomized complete block design (RCBD) with three replications at six locations over the 2018 and 2019 seasons. The study aimed to evaluate the effects of environmental and genotypic variations using MANOVA, PCA, and correlation analysis to discover trait patterns and relationships. The MANOVA results revealed highly significant effects of genotype, environment, and their interaction on the 11 dependent variables (p < 0.001). The four principal components account for 74.23% of the total variation, with key traits such as seed yield per hectare, oil yield per hectare, days to maturity, plant height, and grain filling period significantly contributing to the overall variability. Oil yield per hectare and seed yield per hectare exhibited a very strong association (0.974). Days to maturity (DM) and grain filling period (GFP) showed a strong correlation (0.666), suggesting that longer grain filling periods may enhance both maturity and yield. Additionally, plant height (PH) and seed yield per hectare (YELDK) had a moderate correlation (0.491). Breeding programs should target traits with strong correlations to boost sunflower productivity and adaptability. Future research should prioritize selecting genotypes that perform well across diverse environments, focusing on seed yield, oil yield, and traits such as maturity and grain filling period. Additionally, breeding should incorporate disease resistance and optimize days to flowering to develop more robust and productive sunflower varieties.

VL  - 10
IS  - 3
ER  -

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Author Information

Birhanu Mengistu Aboye

Crop Research Directorate, Ethiopian Institute of Agricultural Research, Adiss Ababa, Ethiopia

Contact Email

http://orcid.org/0009-0009-0041-8291
Tilahun Mola Tesema

Crop Research Directorate, Ethiopian Institute of Agricultural Research, Adiss Ababa, Ethiopia

Contact Email

http://orcid.org/0009-0006-0099-0245

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APA Style

Aboye, B. M., Tesema, T. M. (2024). Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations. American Journal of Biological and Environmental Statistics, 10(3), 87-95. https://doi.org/10.11648/j.ajbes.20241003.16

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ACS Style

Aboye, B. M.; Tesema, T. M. Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations. Am. J. Biol. Environ. Stat. 2024, 10(3), 87-95. doi: 10.11648/j.ajbes.20241003.16

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AMA Style

Aboye BM, Tesema TM. Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations. Am J Biol Environ Stat. 2024;10(3):87-95. doi: 10.11648/j.ajbes.20241003.16

Copy | Download

@article{10.11648/j.ajbes.20241003.16,
  author = {Birhanu Mengistu Aboye and Tilahun Mola Tesema},
  title = {Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations
},
  journal = {American Journal of Biological and Environmental Statistics},
  volume = {10},
  number = {3},
  pages = {87-95},
  doi = {10.11648/j.ajbes.20241003.16},
  url = {https://doi.org/10.11648/j.ajbes.20241003.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbes.20241003.16},
  abstract = {Sunflower is an important agricultural crop valued for its high oil yield, versatility in culinary and industrial applications and adaptability to diverse environments. Eight advanced sunflower genotypes were tested in a randomized complete block design (RCBD) with three replications at six locations over the 2018 and 2019 seasons. The study aimed to evaluate the effects of environmental and genotypic variations using MANOVA, PCA, and correlation analysis to discover trait patterns and relationships. The MANOVA results revealed highly significant effects of genotype, environment, and their interaction on the 11 dependent variables (p < 0.001). The four principal components account for 74.23% of the total variation, with key traits such as seed yield per hectare, oil yield per hectare, days to maturity, plant height, and grain filling period significantly contributing to the overall variability. Oil yield per hectare and seed yield per hectare exhibited a very strong association (0.974). Days to maturity (DM) and grain filling period (GFP) showed a strong correlation (0.666), suggesting that longer grain filling periods may enhance both maturity and yield. Additionally, plant height (PH) and seed yield per hectare (YELDK) had a moderate correlation (0.491). Breeding programs should target traits with strong correlations to boost sunflower productivity and adaptability. Future research should prioritize selecting genotypes that perform well across diverse environments, focusing on seed yield, oil yield, and traits such as maturity and grain filling period. Additionally, breeding should incorporate disease resistance and optimize days to flowering to develop more robust and productive sunflower varieties.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Analyzing Sunflower Trait Patterns Using MANOVA, PCA, and Correlation Across Seasons and Locations

AU  - Birhanu Mengistu Aboye
AU  - Tilahun Mola Tesema
Y1  - 2024/09/29
PY  - 2024
N1  - https://doi.org/10.11648/j.ajbes.20241003.16
DO  - 10.11648/j.ajbes.20241003.16
T2  - American Journal of Biological and Environmental Statistics
JF  - American Journal of Biological and Environmental Statistics
JO  - American Journal of Biological and Environmental Statistics
SP  - 87
EP  - 95
PB  - Science Publishing Group
SN  - 2471-979X
UR  - https://doi.org/10.11648/j.ajbes.20241003.16
AB  - Sunflower is an important agricultural crop valued for its high oil yield, versatility in culinary and industrial applications and adaptability to diverse environments. Eight advanced sunflower genotypes were tested in a randomized complete block design (RCBD) with three replications at six locations over the 2018 and 2019 seasons. The study aimed to evaluate the effects of environmental and genotypic variations using MANOVA, PCA, and correlation analysis to discover trait patterns and relationships. The MANOVA results revealed highly significant effects of genotype, environment, and their interaction on the 11 dependent variables (p < 0.001). The four principal components account for 74.23% of the total variation, with key traits such as seed yield per hectare, oil yield per hectare, days to maturity, plant height, and grain filling period significantly contributing to the overall variability. Oil yield per hectare and seed yield per hectare exhibited a very strong association (0.974). Days to maturity (DM) and grain filling period (GFP) showed a strong correlation (0.666), suggesting that longer grain filling periods may enhance both maturity and yield. Additionally, plant height (PH) and seed yield per hectare (YELDK) had a moderate correlation (0.491). Breeding programs should target traits with strong correlations to boost sunflower productivity and adaptability. Future research should prioritize selecting genotypes that perform well across diverse environments, focusing on seed yield, oil yield, and traits such as maturity and grain filling period. Additionally, breeding should incorporate disease resistance and optimize days to flowering to develop more robust and productive sunflower varieties.

VL  - 10
IS  - 3
ER  -

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